COVID-19 forecasting models have been used to inform decision making around
resource allocation and intervention decisions e.g., hospital beds or
stay-at-home orders. State of the art deep learning models often use multimodal
data such as mobility or socio-demographic data to enhance COVID-19 case
prediction models. Nevertheless, related work has revealed under-reporting bias
in COVID-19 cases as well as sampling bias in mobility data for certain
minority racial and ethnic groups, which could in turn affect the fairness of
the COVID-19 predictions along race labels. In this paper, we show that state
of the art deep learning models output mean prediction errors that are
significantly different across racial and ethnic groups; and which could, in
turn, support unfair policy decisions. We also propose a novel de-biasing
method, DemOpts, to increase the fairness of deep learning based forecasting
models trained on potentially biased datasets. Our results show that DemOpts
can achieve better error parity that other state of the art de-biasing
approaches, thus effectively reducing the differences in the mean error
distributions across more racial and ethnic groups.

我们展示了最先进的深度学习模型在预测 COVID-19 病例时输出的平均预测误差在不同种族和民族群体之间存在显著差异，这可能支持不公平的政策决策。我们还提出了一种新颖的去偏方法 DemOpts，用于提高基于可能存在偏见数据集训练的深度学习预测模型的公平性。我们的结果显示，DemOpts 可以实现更好的误差平等，从而有效降低更多种族和民族群体的平均误差分布差异。